Evidence map›Paper›PMID 30383757›Full record

ArticlePLoS computational biology2018

Efficient pedigree recording for fast population genetics simulation.

Jerome Kelleher, Kevin R Thornton, Jaime Ashander, Peter L Ralph

Abstract read
In one paragraph

Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 132 papers.

0numbers the graph read from it
0cells of the map it votes in
132citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

132 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Recent population history in Swedish cattle breeds.Genetics, selection, evolution : GSE · 2026
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72 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jerome KelleherBig Data Institute, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-7894-5253
Kevin R ThorntonEcology and Evolutionary Biology, University of California, Irvine, Irvine, California, United States of America.ORCID 0000-0003-0743-4445
Jaime AshanderEcology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States of America.ORCID 0000-0002-1841-4768
Peter L RalphInstitute for Ecology and Evolution, University of Oregon, Eugene, Oregon, United States of America.ORCID 0000-0002-9459-6866

Funding

Population genomics of polygenic adaptationR01GM115564 · NIGMS · UNIVERSITY OF CALIFORNIA-IRVINE · PI THORNTON, KEVIN R. · 2015 to 2018
$1.2M
NIGMS NIH HHS R01 GM115564Wellcome TrustWellcome Trust 100956/Z/13/Z
6 · The paper itself

Abstract

In this paper we describe how to efficiently record the entire genetic history of a population in forwards-time, individual-based population genetics simulations with arbitrary breeding models, population structure and demography. This approach dramatically reduces the computational burden of tracking individual genomes by allowing us to simulate only those loci that may affect reproduction (those having non-neutral variants). The genetic history of the population is recorded as a succinct tree sequence as introduced in the software package msprime, on which neutral mutations can be quickly placed afterwards. Recording the results of each breeding event requires storage that grows linearly with time, but there is a great deal of redundancy in this information. We solve this storage problem by providing an algorithm to quickly 'simplify' a tree sequence by removing this irrelevant history for a given set of genomes. By periodically simplifying the history with respect to the extant population, we show that the total storage space required is modest and overall large efficiency gains can be made over classical forward-time simulations. We implement a general-purpose framework for recording and simplifying genealogical data, which can be used to make simulations of any population model more efficient. We modify two popular forwards-time simulation frameworks to use this new approach and observe efficiency gains in large, whole-genome simulations of one to two orders of magnitude. In addition to speed, our method for recording pedigrees has several advantages: (1) All marginal genealogies of the simulated individuals are recorded, rather than just genotypes. (2) A population of N individuals with M polymorphic sites can be stored in O(N log N + M) space, making it feasible to store a simulation's entire final generation as well as its history. (3) A simulation can easily be initialized with a more efficient coalescent simulation of deep history. The software for recording and processing tree sequences is named tskit.

Indexed as

Genetics, PopulationGenetic VariationSoftwareAlgorithmsComputational BiologyComputer SimulationGene FrequencyGenomeGenotypeHumansModels, GeneticPedigreePolymorphism, Genetic

Identifiers

PMID30383757
PMCPMC6233923

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.